An Adaptive Alignment Algorithm for Quality-Controlled Label-Free LC-MS
News Jan 30, 2013
Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multi-user software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.
The article is published online in Molecular & Cellular Proteomics and is free to access.
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.
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A new synthetic enzyme, crafted from DNA rather than protein, flips lipid molecules within the cell membrane, triggering a signal pathway that could be harnessed to induce cell death in cancer cells. Researchers say their lipid-scrambling DNA enzyme is the first in its class to outperform naturally occurring enzymes – and does so by three orders of magnitudeREAD MORE
Antarctic Worm and Machine Learning Help Identify Cerebral Palsy EarlierNews
A research team has released a study in the peer-reviewed journal BMC Bioinformatics showing that DNA methylation patterns in circulating blood cells can be used to help identify spastic cerebral palsy (CP) patients. The technique which makes use of machine learning, data science and even analysis of Antarctic worms, raises hopes for earlier targeted CP therapies.